
DAGStat 2025
24-28 of March 2025
The DeSBi Research Unit organized a dedicated session on its research topics at DAGStat 2025 in Berlin. The session, titled „Fusing Deep Learning and Statistics Towards Understanding Structured Biomedical Data,“ was chaired by our associated postdoctoral researcher, Georg Keilbar. Oral presentations were delivered by unit PhD students and postdocs: Masoumeh Javanbakht, Marco Simancher, Manuel Pfeuffer, and Sepideh Saran.
The presented topics covered a diverse range of cutting-edge research, including deep nonparametric conditional independence tests for images, visual explanations for statistical tests, deep modeling in the presence of known confounders with applications to neuroimaging data, and an empirical analysis of uncertainty quantification in genomics applications.

DeSBi Joint Seminar Series
The RU’s joint seminar series plays a vital role in fostering scientific exchange and enhancing our visibility within the research community. These seminars feature both internal and external speakers, creating a vibrant platform for knowledge sharing.
Join Us!
We invite everyone to participate in the upcoming seminars and engage in the dynamic discussions that shape our research landscape. Stay tuned for our schedule and speaker announcements.
DeSBi Retreat 2024
Our research unit, RU KI-FOR 5363, recently held its annual retreat—an event we eagerly anticipate each year. This retreat serves as a critical platform for promoting scientific exchange and taking full advantage of the diverse expertise within our project teams. More than just a gathering, it’s a space where we come together to discuss the progress of our work, showcase key achievements, and engage in meaningful discussions that drive innovation forward.
KI-FOR 5363 DeSBi
Fusing Deep Learning an Statistics towards Understanding Structured Biomedical Data (DeSBi)
Publications

Paulo Yanez: Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
Published in: Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science
Abstract:
Explainability is a key component in many applications involving deep neural networks (DNNs). However, current explanation methods for DNNs commonly leave it to the human observer to distinguish relevant explanations from spurious noise. This is not feasible anymore when going from easily human-accessible data such as images to more complex data such as genome sequences…
Events
20 March 2024
Jonas Peters (ETH Zurich)
Jonas Peters (ETH Zurich): course on „Causality“, DeSBi Short Courses, Humboldt Univeristy of Berlin, Berlin.
16 January 2024
Jonas Wahl (TU Berlin)
Jonas Wahl (TU Berlin): Presentation on „Causal Inference on Variable Groups and Time Series“, DeSBi Seminar Series, Humboldt University of Berlin, Berlin.
18 December 2023
Xiangnan Xu
Xiangnan Xu: Presentation on „Unravelling complex diet-gut microbiome-host health interaction by mixture of experts models“ (Authors: X. Xu, S. Greven, S. Mueller), Statistical modelling with complex data/ CMStatistics, Berlin.
18 December 2023
Marco Simnacher (P1)
Marco Simnacher (P1): Presentation on „Deep non-parametric conditional independence tests for images“ (Authors: M. Simnacher, X. Xu, H. Park, C. Lippert, S. Greven), Causal Inference/ CMStatistics, Berlin.
17 December 2023
Manuel Pfeuffer (P7)
Manuel Pfeuffer (P7): Presentation on „Cofounder control using semi-structured networks for neuroimaging data“ (Authors: M. Pfeuffer, R. Rane, K. Ritter, S. Greven), Orthogonalization and sparsity in neural networks/ CMStatistics, Berlin.